Decision tree induction from numeric data stream

Satoru Nishimura; Masahiro Terabe; Kazuo Hashimoto

doi:10.1007/978-3-540-89378-3_30

Decision tree induction from numeric data stream

Satoru Nishimura^*, Masahiro Terabe, Kazuo Hashimoto

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Hoeffding Tree Algorithm is known as a method to induce decision trees from a data stream. Treatment of numeric attribute on Hoeffding Tree Algorithm has been discussed for stationary input. It has not yet investigated, however, for non-stationary input where the effect of concept drift is apparent. This paper identifies three major approaches to handle numeric values, Exhaustive Method, Gaussian Approximation, and Discretizaion Method, and through experiment shows the best suited modeling of numeric attributes for Hoeffding Tree Algorithm. This paper also experimentaly compares the performance of two known methods for concept drift detection, Hoeffding Bound Based Method and Accuracy Based Method.

Original language	English
Title of host publication	AI 2008
Subtitle of host publication	Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings
Pages	311-317
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-540-89378-3_30
Publication status	Published - 2008 Dec 1
Externally published	Yes
Event	21st Australasian Joint Conference on Artificial Intelligence, AI 2008 - Auckland, New Zealand Duration: 2008 Dec 1 → 2008 Dec 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5360 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st Australasian Joint Conference on Artificial Intelligence, AI 2008
Country/Territory	New Zealand
City	Auckland
Period	08/12/1 → 08/12/5

Keywords

Concept drift
Hoeffding tree
Numeric data stream

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-89378-3_30

Cite this

Nishimura, S., Terabe, M., & Hashimoto, K. (2008). Decision tree induction from numeric data stream. In AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings (pp. 311-317). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5360 LNAI). https://doi.org/10.1007/978-3-540-89378-3_30

Decision tree induction from numeric data stream. / Nishimura, Satoru; Terabe, Masahiro; Hashimoto, Kazuo.

AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. 2008. p. 311-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5360 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nishimura, S, Terabe, M & Hashimoto, K 2008, Decision tree induction from numeric data stream. in AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5360 LNAI, pp. 311-317, 21st Australasian Joint Conference on Artificial Intelligence, AI 2008, Auckland, New Zealand, 08/12/1. https://doi.org/10.1007/978-3-540-89378-3_30

Nishimura S, Terabe M, Hashimoto K. Decision tree induction from numeric data stream. In AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. 2008. p. 311-317. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-89378-3_30

Nishimura, Satoru ; Terabe, Masahiro ; Hashimoto, Kazuo. / Decision tree induction from numeric data stream. AI 2008: Advances in Artificial Intelligence - 21st Australasian Joint Conference on Artificial Intelligence, Proceedings. 2008. pp. 311-317 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Hoeffding Tree Algorithm is known as a method to induce decision trees from a data stream. Treatment of numeric attribute on Hoeffding Tree Algorithm has been discussed for stationary input. It has not yet investigated, however, for non-stationary input where the effect of concept drift is apparent. This paper identifies three major approaches to handle numeric values, Exhaustive Method, Gaussian Approximation, and Discretizaion Method, and through experiment shows the best suited modeling of numeric attributes for Hoeffding Tree Algorithm. This paper also experimentaly compares the performance of two known methods for concept drift detection, Hoeffding Bound Based Method and Accuracy Based Method.",

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Decision tree induction from numeric data stream

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Keywords

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